# Semantic Scholar Papers Scraper (`neuton/semantic-scholar-papers-scraper`) Actor

Search Semantic Scholar papers by query or paper ID. Export titles, abstracts, authors, venues, dates, citation counts, references, fields of study, external IDs, and open-access PDF links.

- **URL**: https://apify.com/neuton/semantic-scholar-papers-scraper.md
- **Developed by:** [Ashwin Prasad](https://apify.com/neuton) (community)
- **Categories:** Education
- **Stats:** 2 total users, 1 monthly users, 50.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Semantic Scholar Papers Scraper

Search Semantic Scholar papers or fetch known IDs for literature review, RAG ingestion, AI research mapping, and citation intelligence.

Use this actor when you need richer scholarly metadata than a simple title search: abstracts, citation counts, reference counts, fields of study, external IDs, and open-access PDF links. It is useful for research teams, AI/RAG products, academic monitoring, venture diligence, grant scouting, and technical market maps.

### Common use cases

- Build literature-review datasets for AI, biotech, robotics, climate, healthcare, and engineering topics
- Enrich paper IDs, DOI lists, or ArXiv IDs with Semantic Scholar citation metadata
- Monitor fast-moving research topics and citation growth over time
- Feed RAG systems, research assistants, and analyst dashboards with structured paper rows
- Map authors, venues, fields of study, and related work for technical diligence

### Output

Rows include title, abstract, venue, year, publication date, authors, citation counts, reference counts, fields of study, DOI, ArXiv ID, and open-access PDF links.

### Example Input

```json
{"queries":["retrieval augmented generation"],"maxResultsPerQuery":25}
```

For larger jobs, add `semanticScholarApiKey` to increase the upstream Graph API rate limit.

### Pricing Recommendation

Launch with pay-per-event default dataset billing at about $2 per 1,000 paper rows.

### SEO keywords

Semantic Scholar scraper, Semantic Scholar API, academic paper scraper, citation data scraper, research paper metadata, AI literature review dataset, paper citation counts, RAG research data.

### Responsible use

This actor extracts public Semantic Scholar metadata. Respect API limits, paper licenses, and publisher/source rights when using linked PDFs or downstream content. Verify critical research, citation, and bibliographic records before formal publication or regulated decisions.

### Automation ideas

Schedule topic, DOI, or paper-ID refreshes for research teams and AI agents. Agents can summarize new abstracts, detect citation jumps, cluster fields of study, route papers into RAG indexes, and prepare weekly research-intelligence briefs.

# Actor input Schema

## `queries` (type: `array`):

Semantic Scholar search terms, e.g. retrieval augmented generation.

## `paperIds` (type: `array`):

Fetch papers directly by Semantic Scholar ID, DOI:10..., or ARXIV:...

## `maxResultsPerQuery` (type: `integer`):

Maximum papers to export for each search query.

## `semanticScholarApiKey` (type: `string`):

Optional. Add your Semantic Scholar Graph API key to increase rate limits for larger jobs.

## Actor input object example

```json
{
  "queries": [
    "retrieval augmented generation"
  ],
  "maxResultsPerQuery": 10
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("neuton/semantic-scholar-papers-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("neuton/semantic-scholar-papers-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call neuton/semantic-scholar-papers-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=neuton/semantic-scholar-papers-scraper",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/LmJzweqVRRUXEkQj8/builds/omrc3sF79bQ5WOCE0/openapi.json